选择数据框列表的最后一个非 NA 列

Question

我目前正在尝试在 R 中解析 RDP 多分类器层次结构文件，但该问题更普遍适用。基本上我创建了一个列表，其中包含几个文件的数据帧，这些文件包含“分层”行：

dput(corner(hierlist$hier_M2MID06_Trimmed_noGaps.fas_fixrank.txt,n=c(7,10)))
structure(list(X1 = structure(c(30L, 31L, 163L, 45L, 64L, 65L, 
66L), .Label = c("-1071", "-1102", "-1153", "-1159", "-1176", 
"-1177", "-1207", "-1241", "-1256", "-1281", "-1332", "-1353", 
"-1354", "-1502", "-1567", "-18", "-2", "-2715", "-423", "-460", 
"-463", "-471", "-567", "-568", "-828", "-842", "-843", "-871", 
"-980", "0", "1", "1031", "1069", "1070", "1093", "1101", "1126", 
"1151", "1152", "1158", "1159", "1164", "1165", "1166", "1175", 
"1176", "1195", "1200", "1206", "1207", "1215", "1216", "1217", 
"1219", "1240", "1251", "1255", "1256", "1261", "1269", "1279", 
"1280", "1282", "1330", "1331", "1339", "1341", "1343", "1348", 
"1352", "1353", "1354", "1355", "1356", "1357", "1358", "1360", 
"1501", "1566", "16", "1668", "1672", "1674", "17", "1762", "1763", 
"1764", "1767", "1883", "1884", "1885", "1891", "1893", "1894", 
"2", "2164", "2179", "2180", "2183", "2184", "2187", "2192", 
"2195", "2208", "2209", "2210", "2211", "2259", "2260", "2333", 
"2371", "2372", "254", "255", "261", "264", "2684", "2713", "2714", 
"274", "3", "35", "422", "458", "459", "46", "462", "470", "48", 
"49", "54", "565", "566", "567", "570", "577", "581", "648", 
"653", "657", "659", "804", "805", "806", "807", "808", "817", 
"818", "819", "820", "822", "824", "825", "826", "827", "829", 
"832", "834", "837", "838", "839", "840", "841", "842", "843", 
"844", "846", "848", "870", "886", "887", "908", "918", "927", 
"929", "950", "957", "978", "979", "taxid"), class = "factor"), 
X2 = structure(c(3L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Root", 
"lineage", "null"), class = "factor"), X3 = structure(c(1L, 
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Root", "name", "rootrank"
), class = "factor"), X4 = structure(c(2L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = c("Bacteria", "no rank", "rank"), class = "factor"), 
X5 = structure(c(1L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("194", 
"M2MID06_Trimmed_noGaps.fas", "domain"), class = "factor"), 
X6 = structure(c(NA, NA, 10L, 10L, 10L, 10L, 10L), .Label = c("", 
"Acidobacteria", "Actinobacteria", "Bacteroidetes", "Cyanobacteria/Chloroplast", 
"Firmicutes", "Gemmatimonadetes", "Nitrospira", "Planctomycetes", 
"Proteobacteria", "Spirochaetes", "Verrucomicrobia", "unclassified_Bacteria"
), class = "factor"), X7 = structure(c(NA, 2L, 3L, 3L, 3L, 
3L, 3L), .Label = c("", "Bacteria", "phylum"), class = "factor"), 
X8 = structure(c(NA, 21L, NA, 8L, 8L, 8L, 8L), .Label = c("", 
"Acidobacteria_Gp3", "Acidobacteria_Gp4", "Actinobacteria", 
"Alphaproteobacteria", "Bacilli", "Bacteroidetes_incertae_sedis", 
"Betaproteobacteria", "Chloroplast", "Deltaproteobacteria", 
"Flavobacteria", "Gammaproteobacteria", "Gemmatimonadetes", 
"Nitrospira", "Phycisphaerae", "Planctomycetacia", "Sphingobacteria", 
"Spirochaetes", "Subdivision3", "Verrucomicrobiae", "domain", 
"unclassified_Bacteroidetes", "unclassified_Proteobacteria"
), class = "factor"), X9 = structure(c(NA, 2L, 11L, 14L, 
14L, 14L, 14L), .Label = c("", "194", "Acidobacteria", "Actinobacteria", 
"Bacteroidetes", "Cyanobacteria/Chloroplast", "Firmicutes", 
"Gemmatimonadetes", "Nitrospira", "Planctomycetes", "Proteobacteria", 
"Spirochaetes", "Verrucomicrobia", "class", "unclassified_Bacteria"
), class = "factor"), X10 = structure(c(NA, NA, 29L, NA, 
22L, 22L, 22L), .Label = c("", "Actinobacteridae", "Bdellovibrionales", 
"Burkholderiales", "Caulobacterales", "Chloroplast", "Chromatiales", 
"Flavobacteriales", "Gemmatimonadales", "Gp3", "Gp4", "Lactobacillales", 
"Legionellales", "Methylophilales", "Nitrospirales", "Ohtaekwangia", 
"Phycisphaerales", "Planctomycetales", "Pseudomonadales", 
"Rhizobiales", "Rhodobacterales", "Rhodocyclales", "Rhodospirillales", 
"Sphingobacteriales", "Sphingomonadales", "Spirochaetales", 
"Subdivision3_genera_incertae_sedis", "Verrucomicrobiales", 
"phylum", "unclassified_Alphaproteobacteria", "unclassified_Betaproteobacteria", 
"unclassified_Deltaproteobacteria", "unclassified_Gammaproteobacteria"
), class = "factor")), .Names = c("X1", "X2", "X3", "X4", 
"X5", "X6", "X7", "X8", "X9", "X10"), row.names = 2:8, class = "data.frame")

这基本上意味着我有渐进的行，在渐进的列中用 NA 填充。但是，无法确定第一个 NA 将在哪里的特定行。在第一个NA 列之前，我有两列实际上是我感兴趣的：指定分类级别的重叠群数量的计数，以及分类级别名称之前的两列。

我已经创建了一个列表，其中包含每个数据框的索引，该列表将通过以下方式选择最后一行：

library(plyr)
lastcollist<-lapply(hierlist,function(p)lapply(apply(p, 1, function(x) which(!is.na(x)) ),function(x)if(length(x)>0){max(x)}else{0}))
lastcollist<-lapply(lastcollist,unlist)
lastcollist.idx<-llply(lastcollist,function(x)cbind(seq(1,length(x)),x))

这里lastcollist.idx 将包含最后一个非NA 列的每一行的索引：

head(lastcollist.idx$hier_M2MID06_Trimmed_noGaps.fas_fixrank.txt)
        x
[1,] 1  5
[2,] 2  5
[3,] 3  9
[4,] 4 11
[5,] 5 13
[6,] 6 15

所以我现在基本上想要做的是创建一个新列表，其中包含数据框（或者在只有最后一列的情况下，lastcollist.idx 中的变量 x）对于每个给定行都有最后选择的列.

这将是给定示例的所需输出：

 dput(rbind(c('domain','194'),c('Proteobacteria','Phylum'),c('Betaproteobacteria','class'),c ('class','Rhodocyclales'),c('class','Rhodocyclales'),c('class','Rhodocyclales')))
structure(c("domain", "Proteobacteria", "Betaproteobacteria", 
"class", "class", "class", "194", "Phylum", "class", "Rhodocyclales", 
"Rhodocyclales", "Rhodocyclales"), .Dim = c(6L, 2L))

我不得不承认，我不会马上知道该怎么做。任何指针都受到热烈欢迎。我不是 R 的新手，所以你不必费劲地解释。

对于更大的可重现示例，请考虑来自 bioconductor library impute (bioconductor library impute) 的数据集“khanmiss”。

 source("http://bioconductor.org/biocLite.R")
 biocLite("impute")
 require(impute)
 data(khanmiss)

这基本上是一个在多个地方引入了 NA 的数据框。它与我的文件的层次结构不完全相同，但它符合目的。由于这是一个非常不方便的数据框，有 2309 个观察值，并且其中只有 222 行包含缺失值，因此我选择了具有缺失值的行，并在新的 data.frame 中随机添加了 78 行没有缺失值的行。然后，此 data.frame 被拆分为 4 个任意大小的数据帧列表（加起来为 300）。

isnadf<-as.data.frame(which(is.na(khanmiss),arr.ind=T))
na.rows<-sort(unique(isnadf$row))
length(na.rows) #the dataset has 222 rows which contain NA
na.khanmiss<-khanmiss[na.rows,]
notna.rows<-setdiff(rownames(khanmiss),na.rows)
notna.rows.selected<-sort(as.numeric(sample(notna.rows,78)))
notna.selected.khanmiss<-khanmiss[notna.rows.selected,]
khanmiss.selected<-rbind(na.khanmiss,notna.selected.khanmiss)
dfsizes<-c(82,74,79,65) #arbitrarily selected, adds up to 300
khanmiss.list<-split(khanmiss.selected,rep(letters[1:4],dfsizes))

这最终给出了一个与我的数据集有些相似的列表。

Answer 1

这些未经测试的方法可能会起作用：

apply(dfrm, 1, function(r) { r[ (which(is.na(r))[1]-1):(which(is.na(r))[1]-2)) ] } )

对于此示例，我通常将文本输出加载为数据框的方式失败了，因此我的建议是发布 dput 输出而不是屏幕抓取。 （在我看来，您应该使用 header =TRUE 完成数据输入，因为您的第一行数据看起来不像数据。）

有了新数据（并意识到需要测试没有 NA：

 apply(hierlist, 1, function(r) { r[ 
                      if( any(is.na(r))){ 
                          (which(is.na(r))[1]-1):( which(is.na(r))[1]-2) 
                       }else{
                          (length(r)-2): (length(r)-1)}
                                      ] }
       )
#--------------------------------------
     2         3          4                5                   
[1,] "194"     "domain"   "phylum"         "class"             
[2,] "no rank" "Bacteria" "Proteobacteria" "Betaproteobacteria"
     6                    7                    8                   
[1,] "Betaproteobacteria" "Betaproteobacteria" "Betaproteobacteria"
[2,] "class"              "class"              "class"